The present invention relates to the field of computers, and particularly to computers that receive and/or transmit electronic information. Still more particularly, the present invention relates to dynamically modifying electronic information based on eye gaze.
Anticipating user interaction is an important element in human-machine interface design. Guiding a user to perform a targeted action (e.g. make a purchase, download software or follow a call-to-action) is an area of focus across industries.
The success rate of users starting and completing a target digital action can greatly depend on user experience (UX) presentation, how well information about the user is leveraged, and external factors leading up the interaction. A good example of this is online e-commerce websites. The desired target action is a user purchase. The provider of the e-commerce websites makes use of a purchase history of its user base, in addition to other metadata, in order to suggest additional products that may also be of interest to the user, with the goal of maximizing the overall purchase.
Similar practices have been applied across other enterprises, using analytics to segment a user base for delivering targeted content (which may be in the form of products or services) to increase the likelihood of a user performing a target action. Formally defined, user segmentation is the practice of dividing a user base into groups that reflect similarity among users in each group. The goal of segmenting users is to decide how to relate to users in each segment in order to maximize the value of each user to a business.
For instance, certain processes track user behavior across a number of dimensions, and apply analytics to determine the likelihood of a user completing an action, as well as to infer potential blockers to completing the action.
In such products and offerings, the inputs to the system are well-defined. Legacy metrics (for example: click-through rates, page views, view durations, etc.), coupled with any available user and demographic metadata, are used to facilitate user segmentation.
However, no system or associated method exists for using an unsupervised machine learning approach to segment (i.e., cluster) a user base population using eye gaze tracking data to infer optimal (content and format) for delivery to the user.
The present invention provides a new and useful solution of providing such a system and associated methods.